In data communication networks, nodes often communicate by transmitting data bits as analog waveforms over a cable, commonly an unshielded twisted pair (UTP) or shielded twisted pair (STP) cable. A receiver is generally implemented at the receiving node to recover the digital data bits from the analog signal. Because analog signals transmitted over such links are often low in power and radiation, and because of cable tolerances, such signals are often obscured upon arrival at the receiver. The distortion is particularly problematic where the signal is transmitted over a long length of cable at a high frequency or in a multi-level symbol alphabet, such as MLT-3. If left uncorrected, such distortion often renders recovery of the implicit clock impossible and can cause either a total inability to recover the transmitted data, or recovery of data with an unacceptably high incidence of bit errors. Therefore, it is often necessary to improve the quality of the signal before attempting clock or data recovery. Signal quality is typically improved through a process known as equalization which, generally speaking, compensates for distortions and reshapes the signal closer to its original waveform.
The equalization process can present technical challenges because the nature and extent of distortions introduced in data-carrying channels varies from network-to-network and link-to-link. The nature of channel-introduced distortions can be affected by numerous factors, including channel length, transmission frequency and, to a generally lesser extent, impediments in connectors and coupling transformers, manufacturing variations and environmental factors such as temperature. Additional complications arise from the dependence of some distortion-causing variables on others, such as the frequency dependency of signal attenuation for a given channel length. This particular interdependence is modeled by the well-known "square root" attenuation model shown graphically for Category 5 UTP cable in FIG. 1.
Because the distortion-causing factors at work can vary considerably, fixed or static equalization techniques have proven of limited value in correcting distortions introduced in some kinds of data-carrying channels, such as UTP and STP channels. An alternative technique known as adaptive equalization has proven more useful in correcting such distortions. Known adaptive equalizers have implemented equalization techniques, often decision-directed least mean square (LMS) error techniques, which are self-optimizing over one or more variables, such as channel length. Various adaptive equalization techniques have been developed. See, e.g., Honing and Messerschmidt, Adaptive Filters: Structures, Algorithms and Applications; Widrow, Adaptive Filtering; Domer, U.S. Pat. No. 4,583,235; Cherubini, U.S. Pat. No. 5,455,843. While known adaptive equalizers have proven useful tools for improving the resolution of signals transmitted over UTP and STP channels, they have usually been inadequate, especially when applied as the sole equalization source, to enable reliable data recovery in high performance data communication networks. For instance, it is well known that non-fractionally spaced adaptive equalizers are sensitive to baud sampling phase. The distortions introduced in UTP and STP channels, especially at the longer cable lengths, have resulted in signals with a level of inter-symbol interference (ISI) so large that adaptive equalizers have often been unable, acting alone, to process them accurately enough to enable consistent low bit error data recovery. Accordingly, there is a need in the high performance data communication networking field for improved equalization techniques for reforming signals severely distorted by analog transmission over cable, particularly over long or unknown lengths of UTP or STP cable.